"""
YOLO-specific modules

Usage:
    $ python path/to/models/yolo.py --cfg yolov5s.yaml
"""
import argparse
import sys
from copy import deepcopy
from pathlib import Path
FILE = Path(__file__).resolve()
ROOT = FILE.parents[1]
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))
from models.common import *
from models.experimental import *
from models.attention.eca import eca_layer
from models.attention.sa import sa_layer
from models.attention.cbam import CBAM
from utils.autoanchor import check_anchor_order
from utils.general import check_yaml, make_divisible, print_args, set_logging, LOGGER
from utils.plots import feature_visualization
from utils.torch_utils import copy_attr, fuse_conv_and_bn, initialize_weights, model_info, scale_img, select_device, time_sync
try:
    import thop
except ImportError:
    thop = None


class Detect(nn.Module):
    stride = None
    onnx_dynamic = False

    def __init__(self, nc=80, anchors=(), ch=(), inplace=True):
        super().__init__()
        self.nc = nc
        self.no = nc + 5
        self.nl = len(anchors)
        self.na = len(anchors[0]) // 2
        self.grid = [torch.zeros(1)] * self.nl
        self.anchor_grid = [torch.zeros(1)] * self.nl
        self.register_buffer('anchors', torch.tensor(anchors).float().view(
            self.nl, -1, 2))
        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)
        self.inplace = inplace

    def forward(self, x):
        z = []
        for i in range(self.nl):
            x[i] = self.m[i](x[i])
            bs, _, ny, nx = x[i].shape
            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3,
                4, 2).contiguous()
            if not self.training:
                if self.grid[i].shape[2:4] != x[i].shape[2:4
                    ] or self.onnx_dynamic:
                    self.grid[i], self.anchor_grid[i] = self._make_grid(nx,
                        ny, i)
                y = x[i].sigmoid()
                if self.inplace:
                    y[..., 0:2] = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i]
                        ) * self.stride[i]
                    y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]
                else:
                    xy = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i]
                        ) * self.stride[i]
                    wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]
                    y = torch.cat((xy, wh, y[..., 4:]), -1)
                z.append(y.view(bs, -1, self.no))
        return x if self.training else (torch.cat(z, 1), x)

    def _make_grid(self, nx=20, ny=20, i=0):
        d = self.anchors[i].device
        yv, xv = torch.meshgrid([torch.arange(ny).to(d), torch.arange(nx).
            to(d)])
        grid = torch.stack((xv, yv), 2).expand((1, self.na, ny, nx, 2)).float()
        anchor_grid = (self.anchors[i].clone() * self.stride[i]).view((1,
            self.na, 1, 1, 2)).expand((1, self.na, ny, nx, 2)).float()
        return grid, anchor_grid


class Model(nn.Module):

    def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None):
        super().__init__()
        if isinstance(cfg, dict):
            self.yaml = cfg
        else:
            import yaml
            self.yaml_file = Path(cfg).name
            with open(cfg, errors='ignore') as f:
                self.yaml = yaml.safe_load(f)
        ch = self.yaml['ch'] = self.yaml.get('ch', ch)
        if nc and nc != self.yaml['nc']:
            LOGGER.info(
                f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
            self.yaml['nc'] = nc
        if anchors:
            LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}'
                )
            self.yaml['anchors'] = round(anchors)
        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])
        self.names = [str(i) for i in range(self.yaml['nc'])]
        self.inplace = self.yaml.get('inplace', True)
        m = self.model[-1]
        if isinstance(m, Detect):
            s = 256
            m.inplace = self.inplace
            m.stride = torch.tensor([(s / x.shape[-2]) for x in self.
                forward(torch.zeros(1, ch, s, s))])
            m.anchors /= m.stride.view(-1, 1, 1)
            check_anchor_order(m)
            self.stride = m.stride
            self._initialize_biases()
        initialize_weights(self)
        self.info()
        LOGGER.info('')

    def forward(self, x, augment=False, profile=False, visualize=False):
        if augment:
            return self._forward_augment(x)
        return self._forward_once(x, profile, visualize)

    def _forward_augment(self, x):
        img_size = x.shape[-2:]
        s = [1, 0.83, 0.67]
        f = [None, 3, None]
        y = []
        for si, fi in zip(s, f):
            xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.
                max()))
            yi = self._forward_once(xi)[0]
            yi = self._descale_pred(yi, fi, si, img_size)
            y.append(yi)
        y = self._clip_augmented(y)
        return torch.cat(y, 1), None

    def _forward_once(self, x, profile=False, visualize=False):
        y, dt = [], []
        for m in self.model:
            if m.f != -1:
                x = y[m.f] if isinstance(m.f, int) else [(x if j == -1 else
                    y[j]) for j in m.f]
            if profile:
                self._profile_one_layer(m, x, dt)
            x = m(x)
            y.append(x if m.i in self.save else None)
            if visualize:
                feature_visualization(x, m.type, m.i, save_dir=visualize)
        return x

    def _descale_pred(self, p, flips, scale, img_size):
        if self.inplace:
            p[..., :4] /= scale
            if flips == 2:
                p[..., 1] = img_size[0] - p[..., 1]
            elif flips == 3:
                p[..., 0] = img_size[1] - p[..., 0]
        else:
            x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4
                ] / scale
            if flips == 2:
                y = img_size[0] - y
            elif flips == 3:
                x = img_size[1] - x
            p = torch.cat((x, y, wh, p[..., 4:]), -1)
        return p

    def _clip_augmented(self, y):
        nl = self.model[-1].nl
        g = sum(4 ** x for x in range(nl))
        e = 1
        i = y[0].shape[1] // g * sum(4 ** x for x in range(e))
        y[0] = y[0][:, :-i]
        i = y[-1].shape[1] // g * sum(4 ** (nl - 1 - x) for x in range(e))
        y[-1] = y[-1][:, i:]
        return y

    def _profile_one_layer(self, m, x, dt):
        c = isinstance(m, Detect)
        o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0
            ] / 1000000000.0 * 2 if thop else 0
        t = time_sync()
        for _ in range(10):
            m(x.copy() if c else x)
        dt.append((time_sync() - t) * 100)
        if m == self.model[0]:
            LOGGER.info(
                f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  {'module'}"
                )
        LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f}  {m.type}')
        if c:
            LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s}  Total")

    def _initialize_biases(self, cf=None):
        m = self.model[-1]
        for mi, s in zip(m.m, m.stride):
            b = mi.bias.view(m.na, -1)
            b.data[:, 4] += math.log(8 / (640 / s) ** 2)
            b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)
                ) if cf is None else torch.log(cf / cf.sum())
            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)

    def _print_biases(self):
        m = self.model[-1]
        for mi in m.m:
            b = mi.bias.detach().view(m.na, -1).T
            LOGGER.info(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.
                shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))

    def fuse(self):
        LOGGER.info('Fusing layers... ')
        for m in self.model.modules():
            if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
                m.conv = fuse_conv_and_bn(m.conv, m.bn)
                delattr(m, 'bn')
                m.forward = m.forward_fuse
        self.info()
        return self

    def autoshape(self):
        LOGGER.info('Adding AutoShape... ')
        m = AutoShape(self)
        copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'),
            exclude=())
        return m

    def info(self, verbose=False, img_size=640):
        model_info(self, verbose, img_size)

    def _apply(self, fn):
        self = super()._apply(fn)
        m = self.model[-1]
        if isinstance(m, Detect):
            m.stride = fn(m.stride)
            m.grid = list(map(fn, m.grid))
            if isinstance(m.anchor_grid, list):
                m.anchor_grid = list(map(fn, m.anchor_grid))
        return self


def parse_model(d, ch):
    LOGGER.info('\n%3s%18s%3s%10s  %-40s%-30s' % ('', 'from', 'n', 'params',
        'module', 'arguments'))
    anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d[
        'width_multiple']
    na = len(anchors[0]) // 2 if isinstance(anchors, list) else anchors
    no = na * (nc + 5)
    layers, save, c2 = [], [], ch[-1]
    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):
        m = eval(m) if isinstance(m, str) else m
        for j, a in enumerate(args):
            try:
                args[j] = eval(a) if isinstance(a, str) else a
            except NameError:
                pass
        n = n_ = max(round(n * gd), 1) if n > 1 else n
        if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF,
            DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3TR,
            C3SPP, C3Ghost, BottleneckMOB, DepthWiseConv, CBM, CBL,
            BSBottleneck, BSConvU, BSConvS, Conv3BN, InvertedResidual, nn.
            Conv2d, CBAMC3, CBAM, SELayer]:
            c1, c2 = ch[f], args[0]
            if c2 != no:
                c2 = make_divisible(c2 * gw, 8)
            args = [c1, c2, *args[1:]]
            if m in [BottleneckCSP, C3, C3TR, C3Ghost, CBAMC3, CBAM, eca_layer
                ]:
                args.insert(2, n)
                n = 1
        elif m is nn.BatchNorm2d:
            args = [ch[f]]
        elif m is Concat:
            c2 = sum([ch[x] for x in f])
        elif m is Detect:
            args.append([ch[x] for x in f])
            if isinstance(args[1], int):
                args[1] = [list(range(args[1] * 2))] * len(f)
        elif m is Contract:
            c2 = ch[f] * args[0] ** 2
        elif m is Expand:
            c2 = ch[f] // args[0] ** 2
        elif m is eca_layer or m is sa_layer:
            channel = args[0]
            channel = make_divisible(channel * gw, 8
                ) if channel != no else channel
            args = [channel]
        else:
            c2 = ch[f]
        m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args
            )
        t = str(m)[8:-2].replace('__main__.', '')
        np = sum([x.numel() for x in m_.parameters()])
        m_.i, m_.f, m_.type, m_.np = i, f, t, np
        LOGGER.info('%3s%18s%3s%10.0f  %-40s%-30s' % (i, f, n_, np, t, args))
        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x !=
            -1)
        layers.append(m_)
        if i == 0:
            ch = []
        ch.append(c2)
    return nn.Sequential(*layers), sorted(save)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help=
        'model.yaml')
    parser.add_argument('--device', default='', help=
        'cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--profile', action='store_true', help=
        'profile model speed')
    opt = parser.parse_args()
    opt.cfg = check_yaml(opt.cfg)
    print_args(FILE.stem, opt)
    set_logging()
    device = select_device(opt.device)
    model = Model(opt.cfg).to(device)
    model.train()
    if opt.profile:
        img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640
            ).to(device)
        y = model(img, profile=True)
